When processing large-scale point clouds, efficiency is one of the fundamental measurements to evaluate models. Most of the point-based methods utilized point sampling to improve efficiency. However, it is non-trivial to choose an effective point sampling method. For example, Farthest Point Sampling(FPS) which is widely adopted in [1]}, [2]}, [3]}, has \(O(NlogN)\)  computation complexity, meaning it does not have good scalability. Random point sampling as used in RandLA-Net[4]}, has \(O(1)\)  time complexity, but random point sampling cannot be invariant to point densities and key information might be discarded. Other approaches manage to incorporate hybrid representations to avoid the redundant computing and storing of more useful spatial information. A recent work Grid-GCN [5]} proposed a novel method which facilitates grid space structuring and provides more complete coverage of the point cloud. This method is able to handle massive points with fast speed and good scalability.
Point-Voxel CNN [6]} is most related to our method. This work combines fine-grained point features with coarse-grained voxel features with speedup and low memory consumption. Compared to their work, our method builds PVC layers with multi-resolution voxels, and incorporates more accurate local feature aggregation which reduce information loss artifacts.
